IJAT Vol.15 No.3 pp. 350-358
doi: 10.20965/ijat.2021.p0350


Prompt Estimation of Die and Mold Machining Time by AI Without NC Program

Hiroki Takizawa*,†, Hideki Aoyama**, and Song Cheol Won***

*School of Integrated Design Engineering, Keio University
3-14-1 Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa 223-8521, Japan

Corresponding author

**Department of System Design Engineering, Keio University, Yokohama, Japan

***UEL Corporation, Tokyo, Japan

November 9, 2020
January 20, 2021
May 5, 2021
machining time estimation, artificial intelligence, mold, die, no NC-data

Machining time estimation is essential for the due-date estimation of products as well as for production planning. Conventionally, machining time has been estimated by a computer aided manufacturing (CAM) system, which requires time and effort to create its numerical control (NC) program and requires machining expertise to operate it. In addition, among the problems with conventional methods, an error in the estimated machining time arises owing to the machine tool’s control characteristics. In this study, an artificial intelligence (AI)-based system capable of estimating machining time promptly and simply based on shape data without requiring any NC program is developed. The input data to the AI system are color information regarding the machined depths, which are used to estimate the rough-machining time, and color information regarding the machined surface curvature distributions to estimate the finish-machining time. Color information on the machined depths and machined surface curvature distributions is created using three-dimensional computer aided design (3D CAD) data. To build the AI system, the shape data and machining time data accumulated at the machining site are used, so that the machining time estimated reflects the machining method, machining expertise, and the machine tool characteristics employed.

Cite this article as:
H. Takizawa, H. Aoyama, and S. Won, “Prompt Estimation of Die and Mold Machining Time by AI Without NC Program,” Int. J. Automation Technol., Vol.15 No.3, pp. 350-358, 2021.
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Last updated on Jun. 19, 2024